import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
	import torch


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_UpperCAmelCase          =						logging.get_logger(__name__)
_UpperCAmelCase          =						{"""vocab_file""": """spiece.model"""}

_UpperCAmelCase          =						{
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

_UpperCAmelCase          =						{
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}





class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






				lowerCamelCase_        =   VOCAB_FILES_NAMES
				lowerCamelCase_        =   PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_        =   ['''input_ids''', '''attention_mask''']
				def __init__(		self						,      lowercase						,      lowercase=False						,      lowercase=False						,      lowercase=False						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      lowercase = None						,      **lowercase						,      ):



						"""simple docstring"""





						A_						:							int      = {} if sp_model_kwargs is None else sp_model_kwargs

						A_						:							Dict      = kwargs.get('name_or_path'   )
						if name_or_path is None:
								logger.warning(
								    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
								    ' you are testing the model, this can safely be ignored'   )
								A_						:							Optional[int]      = 'None'

						# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
						A_						:							int      = '<|endoftext|>' if eos_token is None else eos_token
						A_						:							int      = '<unk>' if unk_token is None else unk_token
						if "gpt-sw3-7b" in name_or_path:
								A_						:							int      = unk_token if pad_token is None else pad_token
								A_						:							int      = eos_token if bos_token is None else bos_token
						else:
								A_						:							List[str]      = '<pad>' if pad_token is None else pad_token
								A_						:							List[Any]      = '<s>' if bos_token is None else bos_token

						super().__init__(
						    do_lower_case=lowercase						,      remove_space=lowercase						,      keep_accents=lowercase						,      bos_token=lowercase						,      eos_token=lowercase						,      unk_token=lowercase						,      pad_token=lowercase						,      sp_model_kwargs=self.sp_model_kwargs						,      **lowercase						,      )

						A_						:							Union[str, Any]      = do_lower_case
						A_						:							Union[str, Any]      = remove_space
						A_						:							Optional[int]      = keep_accents
						A_						:							List[Any]      = vocab_file

						A_						:							str      = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
						self.sp_model.Load(lowercase   )

						# Used for whitespace normalization in input texts
						# fmt : off
						A_						:							Dict      = {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
						# fmt : on

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						A_						:							Optional[Any]      = re.compile(
						    F'''[{''.join(map(lowercase						,      list(range(0						,      9   )   ) + list(range(1_1						,      3_2   )   ) + list(range(1_2_7						,      1_6_0   )   ) + [1_6_0, 1_7_3, 8_2_0_3]   )   )}]'''   )
				def __getstate__(		self   ):



						"""simple docstring"""





						A_						:							Optional[Any]      = self.__dict__.copy()
						A_						:							str      = None
						return state
				def __setstate__(		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							List[str]      = d

						# for backward compatibility
						if not hasattr(self						,      'sp_model_kwargs'   ):
								A_						:							int      = {}

						A_						:							Any      = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
						self.sp_model.Load(self.vocab_file   )
				@property
				# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						return len(self.sp_model   )
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							str      = self.non_printing_characters_re.sub(''						,      lowercase   )

						# Normalize whitespaces
						A_						:							Tuple      = ''.join([char if char not in self.whitespaces else ' ' for char in text]   )

						# NFC Unicode normalization
						A_						:							Tuple      = unicodedata.normalize('NFC'						,      lowercase   )
						return text
				def     lowerCAmelCase_  (		self						,      lowercase						,      **lowercase   ):



						"""simple docstring"""





						A_						:							List[str]      = self.preprocess_text(lowercase   )
						return self.sp_model.encode(lowercase						,      out_type=lowercase   )
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return self.sp_model.PieceToId(lowercase   )
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return self.sp_model.IdToPiece(lowercase   )
				@staticmethod
				def     lowerCAmelCase_  (		lowercase   ):



						"""simple docstring"""





						return out_string
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							List[str]      = []
						A_						:							Any      = ''
						A_						:							Optional[int]      = False
						for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
										# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
										if not prev_is_special:
												out_string += " "

										out_string += self.sp_model.decode(lowercase   ) + token
										A_						:							Optional[Any]      = True
										A_						:							Optional[int]      = []
								else:
										current_sub_tokens.append(lowercase   )
										A_						:							List[Any]      = False
						out_string += self.sp_model.decode(lowercase   )

						return out_string
				def     lowerCAmelCase_  (		self   ):



						"""simple docstring"""





						A_						:							Tuple      = {self.convert_ids_to_tokens(lowercase   ): i for i in range(self.vocab_size   )}
						vocab.update(self.added_tokens_encoder   )
						return vocab
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = None   ):



						"""simple docstring"""





						if not os.path.isdir(lowercase   ):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
								return
						A_						:							Optional[Any]      = os.path.join(
						    lowercase						,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

						if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowercase   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file						,      lowercase   )
						elif not os.path.isfile(self.vocab_file   ):
								with open(lowercase						,      'wb'   ) as fi:
										A_						:							Tuple      = self.sp_model.serialized_model_proto()
										fi.write(lowercase   )

						return (out_vocab_file,)
				def     lowerCAmelCase_  (		self						,      lowercase						,      lowercase = False   ):



						"""simple docstring"""





						if isinstance(lowercase						,      lowercase   ):
								A_						:							str      = self.preprocess_text(lowercase   )
								A_						:							Dict      = self.sp_model.encode(lowercase   )
						else:
								A_						:							str      = [self.preprocess_text(lowercase   ) for t in text]
								A_						:							Tuple      = self.sp_model.encode(lowercase   )

						if return_tensors is True or return_tensors == "pt":
								A_						:							Optional[int]      = torch.tensor(lowercase   )

						return token_ids
				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						return self.sp_model.decode(lowercase   )







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							Dict      = [F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
						A_						:							Any      = (
						    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowercase   ) + F'''{self.bos_token}Bot:'''
						)
						return self.encode(text=lowercase   )







import sys
import turtle




def 							UpperCamelCase		(			__lowercase		:					tuple[float, float]		,__lowercase		:					tuple[float, float]      ):
		'''simple docstring'''




		return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def 							UpperCamelCase		(			__lowercase		:					tuple[float, float]		,__lowercase		:					tuple[float, float]		,__lowercase		:					tuple[float, float]		,__lowercase		:					int		,):
		'''simple docstring'''




		my_pen.up()
		my_pen.goto(vertexa[0]		,vertexa[1]      )
		my_pen.down()
		my_pen.goto(vertexa[0]		,vertexa[1]      )
		my_pen.goto(vertexa[0]		,vertexa[1]      )
		my_pen.goto(vertexa[0]		,vertexa[1]      )

		if depth == 0:
				return

		triangle(__lowercase		,get_mid(__lowercase		,__lowercase      )		,get_mid(__lowercase		,__lowercase      )		,depth - 1      )
		triangle(__lowercase		,get_mid(__lowercase		,__lowercase      )		,get_mid(__lowercase		,__lowercase      )		,depth - 1      )
		triangle(__lowercase		,get_mid(__lowercase		,__lowercase      )		,get_mid(__lowercase		,__lowercase      )		,depth - 1      )


if __name__ == "__main__":
	if len(sys.argv) != 2:
		raise ValueError(
		    """Correct format for using this script: """
		    """python fractals.py <int:depth_for_fractal>"""
		)
	_UpperCAmelCase          =						turtle.Turtle()
	my_pen.ht()
	my_pen.speed(5)
	my_pen.pencolor("""red""")

	_UpperCAmelCase          =						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
	triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






